In addition to egocentric gaze estimation in the main paper, we also examine the application of our GLC module to the egocentric video action recognition task, and find that our method performs competitively with methods designed specifically for this task on EGTEA Gaze+. To this end, we remove the decoder in the gaze estimation model and keep only the visual token embedding, transformer encoder, and GLC modules. Generally, there are two ways to obtain activity class category prediction: adding a class embedding token at the first layer of transformer, or using pooling across all global tokens to obtain a final embedding. Then a fully-connected layer followed by softmax is used to predict probabilities for each category. We implement both strategies and compare our approaches with previous works in Table REF . We conduct these experiments only on EGTEA Gaze+ [1]} using the same split as gaze estimation. Note that the Ego4D [2]} social benchmark does not contain action labels.
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